TY  - EJOU
AU  - Verma, Mukesh Kumar 
AU  - Yadav, Manohar 

TI  - 3D LiDAR-Based Techniques and Cost-Effective Measures for Precision Agriculture: A Review
T2  - Revue Internationale de Géomatique

PY  - 2025
VL  - 34
IS  - 1
SN  - 2116-7060

AB  - Precision Agriculture (PA) is revolutionizing modern farming by leveraging remote sensing (RS) technologies for continuous, non-destructive crop monitoring. This review comprehensively explores RS systems categorized by platform—terrestrial, airborne, and space-borne—and evaluates the role of multi-sensor fusion in addressing the spatial and temporal complexity of agricultural environments. Emphasis is placed on data from LiDAR, GNSS, cameras, and radar, alongside derived metrics such as plant height, projected leaf area, and biomass. The study also highlights the significance of data processing methods, particularly machine learning (ML) and deep learning (DL), in extracting actionable insights from large datasets. By analyzing the trade-offs between sensor resolution, cost, and application, this paper provides a roadmap for implementing PA technologies. Challenges related to sensor integration, affordability, and technical expertise are also discussed, promoting the development of cost-effective, scalable solutions for sustainable agriculture.
KW  - LiDAR; sensor fusion; 3D crop modelling; precision agriculture

DO  - 10.32604/rig.2025.069914